{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports\n",
    "\n",
    "Import all the modules and functionalities we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Import standard libraries.\n",
    "from IPython.display import Image\n",
    "import os\n",
    "\n",
    "# Import third party libraries.\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "# Import custom libraries/scripts.\n",
    "import annotations\n",
    "import helpers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data\n",
    "\n",
    "Fetch all our relevant data for the current analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set working contants.\n",
    "EXPERIMENTS_PATH = r'E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed'\n",
    "FPS = 60\n",
    "N_MINUTES = 60\n",
    "N_FRAMES = N_MINUTES * 60 * FPS\n",
    "INK = 'black'\n",
    "PPM = 8.8068"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set figure configurations.\n",
    "sns.set(\n",
    "        context='paper',\n",
    "        style='ticks',\n",
    "        font='sans-serif',\n",
    "        font_scale=1.0, \n",
    "        rc={\n",
    "            'axes.axisbelow': True,\n",
    "            'axes.edgecolor': INK,\n",
    "            'axes.facecolor': 'white' if INK=='black' else 'black',\n",
    "            'axes.grid': False,\n",
    "            'axes.labelcolor': INK,\n",
    "            'axes.labelsize': 13.0,\n",
    "            'axes.labelweight': 'normal',\n",
    "            'axes.linewidth': 1.0,\n",
    "            'axes.spines.left': True,\n",
    "            'axes.spines.bottom': True,\n",
    "            'axes.spines.top': False,\n",
    "            'axes.spines.right': False,\n",
    "            'axes.titlepad': 15.0,\n",
    "            'axes.titlesize': 20.0,\n",
    "            'axes.titleweight': 'bold',\n",
    "            'figure.facecolor': 'white' if INK=='black' else 'black',\n",
    "            'figure.figsize': [4.0, 4.0],\n",
    "            'figure.titlesize': 30.0,\n",
    "            'figure.titleweight': 'bold',\n",
    "            'font.family': ['sans-serif'],\n",
    "            'font.sans-serif': ['Arial'],\n",
    "            'legend.frameon': False,\n",
    "            'legend.fontsize': 11.0,\n",
    "            'lines.color': INK,\n",
    "            'lines.linewidth': 1.0,\n",
    "            'patch.edgecolor': INK,\n",
    "            'savefig.dpi': 300,\n",
    "            'savefig.format': 'png',\n",
    "            'savefig.bbox': 'tight',\n",
    "            'savefig.transparent': True,\n",
    "            'text.color': INK,\n",
    "            'text.usetex': False,\n",
    "            'xtick.color': INK,\n",
    "            'xtick.direction': 'out',\n",
    "            'xtick.labelsize': 12.0,\n",
    "            'xtick.major.pad': 5.0,\n",
    "            'xtick.major.size': 0.0,\n",
    "            'xtick.major.width': 1.0,\n",
    "            'xtick.minor.size': 0.0,\n",
    "            'xtick.minor.width': 0.4,\n",
    "            'ytick.color': INK,\n",
    "            'ytick.direction': 'out',\n",
    "            'ytick.labelsize': 12.0,\n",
    "            'ytick.major.pad': 5.0,\n",
    "            'ytick.major.size': 3.0,\n",
    "            'ytick.major.width': 1.0,\n",
    "            'ytick.minor.size': 0.0,\n",
    "            'ytick.minor.width': 0.4\n",
    "           }\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder already exists, skipping.\n"
     ]
    }
   ],
   "source": [
    "# Prepare the Figures folder to save our graphs in.\n",
    "savepath = os.path.join(r'C:\\Users\\Miguel\\Desktop\\paper_data', 'paper_figures', 'figure1')\n",
    "try:\n",
    "    os.makedirs(savepath)\n",
    "    print('New folder created.')\n",
    "except FileExistsError:\n",
    "    print('Folder already exists, skipping.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paths to conditions:\n",
      " ['E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin']\n"
     ]
    }
   ],
   "source": [
    "# Set the conditions to analyze.\n",
    "condition_order = ['virgin_virgin']\n",
    "conditions = [item.path for item in os.scandir(EXPERIMENTS_PATH) if item.name in condition_order]\n",
    "print('Paths to conditions:\\n', conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['video_2017-09-05T15_42_04_arena3', 'video_2017-09-07T13_28_02_arena4']\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# Create a list of all the experiments that we DO NOT want to analyze.\n",
    "intruders = []\n",
    "for condition in conditions:\n",
    "    for file in os.listdir(condition):\n",
    "        if 'quality_control' in file:\n",
    "\n",
    "            # Read the quality.csv file to check which experiments are usable for analysis.\n",
    "            quality_file = os.path.join(condition, file)\n",
    "            quality_df = pd.read_csv(quality_file, usecols=[0,1], index_col=0)\n",
    "            quality_df = quality_df[quality_df['is_usable'] == False].index.values\n",
    "            \n",
    "            for value in quality_df:\n",
    "                intruders.append(value)\n",
    "\n",
    "print(intruders)             \n",
    "print(len(intruders))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\n",
      " virgin_virgin\n",
      "Copulation too short: video_2017-09-06T13_42_13_arena3\n",
      "Copulation too short: video_2017-09-26T14_52_18_arena3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'virgin_virgin': ['E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-05T13_28_41_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-05T14_34_55_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-06T13_42_13_arena1',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-06T14_45_32_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-07T14_37_38_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-07T14_37_38_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-08T13_26_13_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-12T13_30_06_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-12T14_36_55_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-12T16_18_15_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-13T14_34_18_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-20T13_43_53_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-20T14_48_55_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-21T13_38_55_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-21T15_57_23_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-26T13_40_19_arena1',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-28T13_36_06_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-28T14_44_01_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-09-28T15_55_49_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-12T15_50_43_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-13T16_06_33_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-18T15_19_17_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-19T13_50_56_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-20T13_21_06_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-20T15_44_09_arena2',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-24T16_13_35_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-10-25T14_55_12_arena1',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-11-02T13_37_48_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-11-02T14_48_28_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-11-02T16_11_06_arena3',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-11-03T14_54_45_arena1',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-11-03T16_20_50_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-12-20T12_58_51_arena4',\n",
       "  'E:\\\\Miguel\\\\PhD\\\\Results\\\\Competition\\\\DL\\\\Four-Arena Setup\\\\WT_mating_status\\\\processed\\\\virgin_virgin\\\\video_2017-12-20T14_01_01_arena3']}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load all usable experiments for each condition.\n",
    "experiments = {condition: [] for condition in condition_order}\n",
    "\n",
    "for condition_path in conditions:\n",
    "    \n",
    "    condition = os.path.basename(condition_path)\n",
    "    print('\\t\\n', condition)\n",
    "    \n",
    "    for item in os.scandir(condition_path):\n",
    "        if item.is_dir() and item.name not in intruders:\n",
    "            \n",
    "            annotation_video = annotations.read(item.path + '.csv')\n",
    "            \n",
    "            try:\n",
    "                copulation = annotation_video[0].events[0]\n",
    "\n",
    "                # Filter out videos where copulation is interrupted.\n",
    "                copulation_end = copulation.time_interval[1]\n",
    "                if copulation_end==N_FRAMES:\n",
    "                    print('Copulation interrupted:', item.name)\n",
    "                    continue\n",
    "\n",
    "                # Filter out videos where copulation lasts less than 8 minutes.\n",
    "                copulation_duration = copulation.duration\n",
    "                if copulation_duration <= 8 * 60 * FPS:\n",
    "                    print('Copulation too short:', item.name)\n",
    "                    continue\n",
    "            \n",
    "            except IndexError:\n",
    "                continue\n",
    "            \n",
    "            experiments[condition].append(item.path)\n",
    "\n",
    "experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\n",
      "Normalized Polar Histogram Test\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test the plotting function.\n",
    "n = 100000\n",
    "a = np.random.normal(loc=0, scale=60, size=n)\n",
    "a = np.deg2rad(a)\n",
    "r = np.random.normal(loc=4, scale=1, size=n)\n",
    "\n",
    "print('\\t\\nNormalized Polar Histogram Test\\n')\n",
    "helpers.plot_polar_histogram(a, mode='normalized', theta_zero_loc='W', rlabels_pos=225, radial_limit=0.08, radial_bins=4)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - FEMALE DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get trackfeat data from the mating and aggressive females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-05T13_28_41_arena4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miguel\\Desktop\\paper_data\\paper_code\\figure1\\Four-Arena Setup\\WT_mating_status\\helpers.py:647: RuntimeWarning: invalid value encountered in arccos\n",
      "  theta_center_body_point = np.rad2deg(np.arccos(center_to_new_intersection / ellipse_point_to_center))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-05T14_34_55_arena2\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-06T13_42_13_arena1\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-06T14_45_32_arena3\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-07T14_37_38_arena3\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-07T14_37_38_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-08T13_26_13_arena3\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-12T13_30_06_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-12T14_36_55_arena3\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-12T16_18_15_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-13T14_34_18_arena2\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-20T13_43_53_arena3\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-20T14_48_55_arena2\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-21T13_38_55_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-21T15_57_23_arena2\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-26T13_40_19_arena1\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-28T13_36_06_arena4\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-28T14_44_01_arena3\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-09-28T15_55_49_arena3\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-12T15_50_43_arena2\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-13T16_06_33_arena2\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-18T15_19_17_arena3\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-19T13_50_56_arena2\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-20T13_21_06_arena2\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-20T15_44_09_arena2\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-24T16_13_35_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-10-25T14_55_12_arena1\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = nan \n",
      "y2 = nan\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 44.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 43.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 44.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 44.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 40.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 40.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 48.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 40.0\n",
      "Y intersects do not match:\n",
      "y1 = 43.00654 \n",
      "y2 = 40.0\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-11-02T13_37_48_arena3\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-11-02T14_48_28_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-11-02T16_11_06_arena3\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-11-03T14_54_45_arena1\n",
      "No aggression!\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-11-03T16_20_50_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-12-20T12_58_51_arena4\n",
      "\n",
      " E:\\Miguel\\PhD\\Results\\Competition\\DL\\Four-Arena Setup\\WT_mating_status\\processed\\virgin_virgin\\video_2017-12-20T14_01_01_arena3\n"
     ]
    }
   ],
   "source": [
    "# Set features to extract from trackfeat files.\n",
    "features = ['pos x', 'pos y', 'ori', 'major axis len', 'minor axis len', 'vel', 'ang_vel', 'dist_to_other', 'angle_between', 'facing_angle']\n",
    "\n",
    "female_frame_info = {condition: {os.path.basename(experiment): {'n_total_frames': 0, 'n_valid_frames': 0} for experiment in experiments[condition]} for condition in condition_order}\n",
    "\n",
    "aggression_to_female_df = pd.DataFrame()\n",
    "mating_female_df = pd.DataFrame()\n",
    "\n",
    "for condition in condition_order:\n",
    "    \n",
    "    for experiment in experiments[condition]:\n",
    "        \n",
    "        print('\\n', experiment)\n",
    "        \n",
    "        control = True if condition == 'virgin_virgin' else False\n",
    "\n",
    "        # Get female tracking data.\n",
    "        female_aggression_df, female_df, _, aggression_timepoints = helpers.process_track_data(experiment, trackfeat_columns=features, is_control=control, fly_id=2, suppress=True)\n",
    "        if female_df is None:\n",
    "            print('No aggression!')\n",
    "            continue\n",
    "        \n",
    "        # Get male tracking data.\n",
    "        male_aggression_df, male_df, _, _ = helpers.process_track_data(experiment, trackfeat_columns=features, is_control=control, fly_id=1, suppress=True)\n",
    "\n",
    "        # Transform some of the features: orientation to degrees, and correct flips.\n",
    "        female_aggression_df.fillna(method='ffill', inplace=True)\n",
    "        female_aggression_df['major_axis_len_mm'] = female_aggression_df['major_axis_len'] / PPM\n",
    "        female_aggression_df['minor_axis_len_mm'] = female_aggression_df['minor_axis_len'] / PPM\n",
    "        female_aggression_df['ori_deg'] = female_aggression_df['ori'] * 180 / np.pi\n",
    "        female_aggression_df['angle_between_deg'] = female_aggression_df['angle_between'] * 180 / np.pi\n",
    "        female_aggression_df['facing_angle_deg'] = female_aggression_df['facing_angle'] * 180 / np.pi\n",
    "        female_aggression_df['ori_deg'] = helpers.fix_orientation_flips(female_aggression_df['ori_deg'])\n",
    "        female_aggression_df['angle_between_deg'] = helpers.fix_orientation_flips(female_aggression_df['angle_between_deg'])\n",
    "        female_aggression_df['facing_angle_deg'] = helpers.fix_orientation_flips(female_aggression_df['facing_angle_deg'])\n",
    "\n",
    "        female_df.fillna(method='ffill', inplace=True)\n",
    "        female_df['major_axis_len_mm'] = female_df['major_axis_len'] / PPM\n",
    "        female_df['minor_axis_len_mm'] = female_df['minor_axis_len'] / PPM\n",
    "        female_df['ori_deg'] = female_df['ori'] * 180 / np.pi\n",
    "        female_df['angle_between_deg'] = female_df['angle_between'] * 180 / np.pi\n",
    "        female_df['facing_angle_deg'] = female_df['facing_angle'] * 180 / np.pi\n",
    "        female_df['ori_deg'] = helpers.fix_orientation_flips(female_df['ori_deg'])\n",
    "        female_df['angle_between_deg'] = helpers.fix_orientation_flips(female_df['angle_between_deg'])\n",
    "        female_df['facing_angle_deg'] = helpers.fix_orientation_flips(female_df['facing_angle_deg'])\n",
    "        \n",
    "        male_aggression_df.fillna(method='ffill', inplace=True)\n",
    "        male_aggression_df['major_axis_len_mm'] = male_aggression_df['major_axis_len'] / PPM\n",
    "        male_aggression_df['minor_axis_len_mm'] = male_aggression_df['minor_axis_len'] / PPM\n",
    "        male_aggression_df['ori_deg'] = male_aggression_df['ori'] * 180 / np.pi\n",
    "        male_aggression_df['angle_between_deg'] = male_aggression_df['angle_between'] * 180 / np.pi\n",
    "        male_aggression_df['facing_angle_deg'] = male_aggression_df['facing_angle'] * 180 / np.pi\n",
    "        male_aggression_df['ori_deg'] = helpers.fix_orientation_flips(male_aggression_df['ori_deg'])\n",
    "        male_aggression_df['angle_between_deg'] = helpers.fix_orientation_flips(male_aggression_df['angle_between_deg'])\n",
    "        male_aggression_df['facing_angle_deg'] = helpers.fix_orientation_flips(male_aggression_df['facing_angle_deg'])\n",
    "\n",
    "        male_df.fillna(method='ffill', inplace=True)\n",
    "        male_df['major_axis_len_mm'] = male_df['major_axis_len'] / PPM\n",
    "        male_df['minor_axis_len_mm'] = male_df['minor_axis_len'] / PPM\n",
    "        male_df['ori_deg'] = male_df['ori'] * 180 / np.pi\n",
    "        male_df['angle_between_deg'] = male_df['angle_between'] * 180 / np.pi\n",
    "        male_df['facing_angle_deg'] = male_df['facing_angle'] * 180 / np.pi\n",
    "        male_df['ori_deg'] = helpers.fix_orientation_flips(male_df['ori_deg'])\n",
    "        male_df['angle_between_deg'] = helpers.fix_orientation_flips(male_df['angle_between_deg'])\n",
    "        male_df['facing_angle_deg'] = helpers.fix_orientation_flips(male_df['facing_angle_deg'])\n",
    "\n",
    "        # Store each fly's tracking data.\n",
    "        female_behaviors = {'aggressive': female_aggression_df, 'mating': female_df}\n",
    "        male_behaviors = {'aggressive': male_aggression_df, 'mating': male_df}\n",
    "\n",
    "        # Calculate current experiment's total amount of aggression frames.\n",
    "        total_frames = sum([event[1] - event[0] + 1 for event in aggression_timepoints])\n",
    "        \n",
    "        # Store indices of valid frames.\n",
    "        valid_frames = []\n",
    "        \n",
    "        # For each aggression bout of the current female, get parameters for all frames\n",
    "        # where the mating ellipse and aggressive major axis intersect.\n",
    "        for n, event in enumerate(aggression_timepoints):\n",
    "\n",
    "            for frame in range(event[0], event[1]+1):\n",
    "\n",
    "                # Calculate mating ellipse parameters.\n",
    "                female_params = helpers.get_parameters(frame, female_behaviors, PPM, inner_color='firebrick')\n",
    "                male_params = helpers.get_parameters(frame, male_behaviors, PPM, inner_color='steelblue')\n",
    "                    \n",
    "                # If the aggressive female is actually targeting the male, skip this frame.   \n",
    "                try:\n",
    "                    if female_params['dist_to_aggressor'] > male_params['dist_to_aggressor']:\n",
    "                        continue\n",
    "                except TypeError:\n",
    "                    pass  # This means that only the male_params returned None, but there is at least some data regarding the female.\n",
    "        \n",
    "                valid_frames.append(frame)\n",
    "\n",
    "        assert len(valid_frames) <= total_frames\n",
    "        \n",
    "        # Store data into the general DataFrame.\n",
    "        female_aggression_df = female_aggression_df.copy().loc[valid_frames, :]\n",
    "        female_aggression_df['condition'] = condition\n",
    "        aggression_to_female_df = pd.concat([aggression_to_female_df, female_aggression_df], axis=0)\n",
    "        \n",
    "        female_df = female_df.copy().loc[valid_frames, :]\n",
    "        female_df['condtion'] = condition\n",
    "        mating_female_df = pd.concat([mating_female_df, female_df], axis=0)\n",
    "        \n",
    "        # Store frame quantification into the general dictionary.\n",
    "        female_frame_info[condition][os.path.basename(experiment)]['n_total_frames'] = total_frames\n",
    "        female_frame_info[condition][os.path.basename(experiment)]['n_valid_frames'] = len(valid_frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'virgin_virgin': 25}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_sample_sizes = {condition: 0 for condition in condition_order}\n",
    "\n",
    "for condition in condition_order:\n",
    "    \n",
    "    for experiment in female_frame_info[condition].keys():\n",
    "        \n",
    "        if female_frame_info[condition][experiment]['n_valid_frames'] > 0:\n",
    "            \n",
    "            female_sample_sizes[condition] += 1\n",
    "    \n",
    "female_sample_sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the spacial distribution of Angles Between the two females?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some reason, FlyTracker outputs all angles in positive radians, which then means we only have access to a 0º to 180º range. This is why the resulting plot only fills half of the angle space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for condition in condition_order:\n",
    "    \n",
    "    current_female_data = aggression_to_female_df[aggression_to_female_df['condition']==condition]\n",
    "    current_female_data = current_female_data[current_female_data['dist_to_other'] < 4] # Filter out extreme distance values.\n",
    "\n",
    "    female_angles = current_female_data['angle_between'] * -1\n",
    "    female_radius = current_female_data['dist_to_other']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\n",
      " virgin_virgin \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_limits = [0.15, 0.15, 0.25]\n",
    "\n",
    "for n, condition in enumerate(condition_order):\n",
    "    \n",
    "    print('\\t\\n', condition, '\\n')\n",
    "    \n",
    "    current_female_data = aggression_to_female_df[aggression_to_female_df['condition']==condition]\n",
    "    current_female_data = current_female_data[current_female_data['dist_to_other'] < 4] # Filter out extreme distance values.\n",
    "\n",
    "    female_angles = current_female_data['angle_between'] * -1\n",
    "    female_radius = current_female_data['dist_to_other']\n",
    "    \n",
    "    # Draw the polar histogram using normalized frequency counts.\n",
    "    axis = helpers.plot_polar_histogram(female_angles, mode='normalized', theta_zero_loc='W', rlabels_pos=185, radial_limit=y_limits[n], radial_bins=5, ink=INK)\n",
    "    \n",
    "    # Saving parameters.\n",
    "    filename = 'angle_between_female_normalized_polar_histogram_' + condition\n",
    "    plt.savefig(os.path.join(savepath, filename))\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the spacial distribution of Facing Angles of the aggressive female towards the mating female?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some reason, FlyTracker outputs all angles in positive radians, which then means we only have access to a 0º to 180º range. This is why the resulting plot only fills half of the angle space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\n",
      " virgin_virgin \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_limits = [0.20, 0.20, 0.15]\n",
    "\n",
    "for n, condition in enumerate(condition_order):\n",
    "\n",
    "    print('\\t\\n', condition, '\\n')\n",
    "    \n",
    "    current_female_data = aggression_to_female_df[aggression_to_female_df['condition']==condition]\n",
    "    current_female_data = current_female_data[current_female_data['dist_to_other'] < 4] # Filter out extreme distance values.\n",
    "    \n",
    "    female_angles = current_female_data['facing_angle'] * -1\n",
    "    female_radius = current_female_data['dist_to_other']\n",
    "    \n",
    "    # Draw the polar histogram using normalized frequency counts.\n",
    "    axis = helpers.plot_polar_histogram(female_angles, mode='normalized', theta_zero_loc='W', rlabels_pos=185, radial_limit=y_limits[n], radial_bins=5, ink=INK)\n",
    "    \n",
    "    # Saving parameters.\n",
    "    filename = 'facing_angle_female_normalized_polar_histogram_' + condition\n",
    "    plt.savefig(os.path.join(savepath, filename))\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
